PUBLIC HEALTH 1 PG APA 3 references on SCIENTIFIC METHOD

Chapter 1

Measurement

October 14

In Chapter 1

1.1 What is Biostatistics? 1.2 Organization of Data? 1.3 Types of Measurements 1.4 Data Quality

Biostatistics • Statistics is not merely a compilation of

computational techniques • Statistics

– is a way of learning from data – is concerned with all elements of study design,

data collection and analysis of numerical data – does require judgment

• Biostatistics is statistics applied to biological and health problems

Biostatisticians are:

• Data detectives – who uncover patterns and clues – This involves exploratory data analysis

(EDA) and descriptive statistics • Data judges

– who judge and confirm clues – This involves statistical inference

Measurement • Measurement (defined): the assigning of

numbers and codes according to prior-set rules (Stevens, 1946).

• There are three broad types of measurements: – Categorical – Ordinal – Quantitative

Measurement Scales • Categorical - classify observations into named

categories, – e.g., HIV status classified as “positive” or

“negative” • Ordinal - categories that can be put in rank order

– e.g., Stage of cancer classified as stage I, stage II, stage III, stage IV

• Quantitative – true numerical values that can be put on a number line – e.g., age (years) – e.g., Serum cholesterol (mg/dL)

Illustrative Example: Weight Change and Heart Disease

• This study sought to determine the effect of weight change on coronary heart disease risk.

• It studied 115,818 women 30- to 55-years of age, free of CHD over 14 years.

• Measurements included – Body mass index (BMI) at study entry – BMI at age 18 – CHD case onset (yes or no)

Source: Willett et al., 1995

Illustrative Example (cont.) Examples of Variables

• Smoker (current, former, no) • CHD onset (yes or no) • Family history of CHD (yes or no) • Non-smoker, light-smoker, moderate

smoker, heavy smoker • BMI (kgs/m3) • Age (years) • Weight presently • Weight at age 18

Quantitative

Categorical

Ordinal

Variable, Value, Observation

• Observation ≡ the unit upon which measurements are made, can be an individual or aggregate

• Variable ≡ the generic thing we measure – e.g., AGE of a person – e.g., HIV status of a person

• Value ≡ a realized measurement – e.g., “27” – e.g., “positive”

Figure 1.1 Four observations with five variables each

Data Table AGE SEX HIV ONSET INFECT 24 M Y 12-OCT-07 Y 14 M N 30-MAY-05 Y 32 F N 11-NOV-06 N

• Each row corresponds to an observation • Each column contains information on a variable • Each cell in the table contains a value

Unit of observation

in these data are

individual regions, not individual people.

Data Quality • An analysis is only as good as its data • GIGO ≡ garbage in, garbage out • Does a variable measure what it purports to?

– Validity = freedom from systematic error – Objectivity = seeing things as they are without

making it conform to a worldview

• Consider how the wording of a question can influence validity and objectivity

Choose Your Ethos

• BS is manipulative and has a predetermined outcome.

• Science “bends over backwards” to consider alternatives.

Scientific Ethos “I cannot give any scientist of any age any

better advice than this: The intensity of the conviction that a hypothesis is true has

no bearing on whether it is true or not.” Peter Medawar